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Chlorine disinfection of biologically treated wastewater is practiced in many locations prior to environmental discharge or beneficial reuse. The effectiveness of chlorine disinfection processes may be influenced by several factors, such as pH, temperature, ionic strength, organic carbon concentration, and suspended solids. We investigated the use of Bayesian multilayer perceptron (BMLP) models as efficient and practical tools for compiling and analysing free chlorine and monochloramine virus disinfection performance as a multivariate problem. Corresponding to their relative susceptibility, Adenovirus 2 was used to assess disinfection by monochloramine and Coxsackievirus B5 was used for free chlorine. A BMLP model was constructed to relate key disinfection conditions (CT, pH, turbidity) to observed Log Reduction Values (LRVs) for these viruses at constant temperature. The models proved to be valuable for incorporating uncertainty in the chlor(am)ination performance estimation and interpolating between operating conditions. Various types of queries could be performed with this model including the identification of target CT for a particular combination of LRV, pH and turbidity. Similarly, it was possible to derive achievable LRVs for combinations of CT, pH and turbidity. These queries yielded probability density functions for the target variable reflecting the uncertainty in the model parameters and variability of the input variables. The disinfection efficacy was greatly impacted by pH and to a lesser extent by turbidity for both types of disinfections. Non-linear relationships were observed between pH and target CT, and turbidity and target CT, with compound effects on target CT also evidenced. This work demonstrated that the use of BMLP models had considerable ability to improve the resolution and understanding of the multivariate relationships between operational parameters and disinfection outcomes for wastewater treatment.

An Australian water utility has developed a Legionella High Level Risk Assessment (LHLRA) which provides a semi-qualitative assessment of the risk of Legionella proliferation and human exposure in engineered water systems using a combination of empirical observation and expert knowledge. Expanding on this LHLRA, we propose two iterative Bayesian network (BN) models to reduce uncertainty and allow for a probabilistic representation of the mechanistic interaction of the variables, built using data from 25 groundwater treatment plants. The risk of Legionella exposure in groundwater aeration units was quantified as a function of five critical areas including hydraulic conditions, nutrient availability and growth, water quality, system design (and maintenance), and location and access. First, the mechanistic relationship of the variables was conceptually mapped into a fishbone diagram, parameterised deterministically using an expert elicited weighted scoring system and translated into BN. The "sensitivity to findings" analysis of the BN indicated that system design was the most influential variable while elemental accumulation thresholds were the least influential variable for Legionella exposure. The diagnostic inference was used in high and low-risk scenarios to demonstrate the capabilities of the BNs to examine probable causes for diverse conditions. Subsequently, the causal relationship of Legionella growth and human exposure were improved through a conceptual bowtie representation. Finally, an improved model developed the predictors of Legionella growth and the risk of human exposure through the interaction of operational, water quality monitoring, operational parameters, and asset conditions. The use of BNs modelling based on risk estimation and improved functional decision outputs offer a complementary and more transparent alternative approach to quantitative analysis of uncertainties than the current LHLRA.

There has been an ongoing dilemma for agencies that set criteria for safe recreational waters in how to provide for a seasonal assessment of a beach site versus guidance for day-to-day management. Typically an overall 'safe' criterion level is derived from epidemiologic studies of sewage-impacted beaches. The decision criterion is based on a percentile value for a single sample or a moving median of a limited number (e.g. five per month) of routine samples, which are reported at least the day after recreator exposure has occurred. The focus of this paper is how to better undertake day-to-day recreational site monitoring and management. Internationally, good examples exist where predictive empirical regression models (based on rainfall, wind speed/direction, etc.) may provide an estimate of the target faecal indicator density for the day of exposure. However, at recreational swimming sites largely impacted by non-sewage sources of faecal indicators, there is concern that the indicator-illness associations derived from studies at sewage-impacted beaches may be inappropriate. Furthermore, some recent epidemiologic evidence supports the relationship to gastrointestinal (GI) illness with qPCR-derived measures of Bacteroidales/Bacteroides spp. as well as more traditional faecal indicators, but we understand less about the environmental fate of these molecular targets and their relationship to bather risk. Modelling pathogens and indicators within a quantitative microbial risk assessment framework is suggested as a way to explore the large diversity of scenarios for faecal contamination and hydrologic events, such as from waterfowl, agricultural animals, resuspended sediments and from the bathers themselves. Examples are provided that suggest that more site-specific targets derived by QMRA could provide insight, directly translatable to management actions.

Quantitative microbial health risk assessment requires accurate enumeration of pathogens in hazard-containing matrices as part of the risk characterization process. As part of a risk management-oriented study of cattle feedlot waste contaminants, we investigated the utility of quantitative real-time PCR (qPCR) for surveying the microbial constituents of different faecal wastes. The abundance of Escherichia coli and enterococci were first estimated in five cattle feedlot waste types from five localities. Bacteria were quantified using two culture methods and compared to the number of genome copies detected by qPCR targeted at E. coli and Enterococcus faecalis . Bacterial numbers detected in the different wastes (fresh faeces, pen manure, aged manure, composted manure, carcass manure compost) ranged from 10− 7 to 10 2 g −1 (dry weight). Both indicator groups were detected by qPCR with a comparable sensitivity to culture methods across this range. qPCR measurements of E. coli and E. faecalis correlated well with MPN and spread plate data. As a second comparison, we inoculated green fluorescent protein (GFP) labeled reference bacteria into manure samples. GFP labeled E. coli and Listeria monocytogenes were detected by qPCR in concentrations corresponding to between 18% and 71% of the initial bacterial numbers, compared to only 2.5–16% by plating. Our results supported our selection of qPCR as a fast, accurate and reliable system for surveying the presence and abundance of pathogens in cattle waste.

Extensive sampling of the built environment was undertaken to determine the extent of Legionella presence in different regions of New South Wales. Distribution and abundance of Legionella in N.S.W. were found to be similar to those found in Europe and North America, despite Australia's warmer climate and geographical isolation. Air conditioning cooling towers, warm water systems and spas showed the highest incidence of Legionella (32, 37 and 36%, respectively). Legionella pneumophila serotypes predominated, being present in 82% of the positive samples examined. Seasonality in isolation patterns was not detected. The Illawarra Health Region, where a major outbreak has recently occurred, was found to have the lowest incidence of Legionella , probably due to improved maintenance measures undertaken in the aftermath of the outbreak. It was concluded that the distribution and abundance of Legionella in the south-eastern part of Australia follows a comparable pattern to elsewhere in the world and that measures to prevent outbreaks of Legionnaires' disease should be similar to those currently being introduced in the U.S.A. and Great Britain.